期刊
JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE
卷 12, 期 9, 页码 2937-2942出版社
AMER SCIENTIFIC PUBLISHERS
DOI: 10.1166/jctn.2015.4203
关键词
Fruit Recognition; Compressed Sensing; Feature Extraction
类别
资金
- Zhejiang Provincial Natural Science Foundation of China [LY12F01017]
- construct program of the key discipline in Hangzhou
- Student innovation fund of national level [201313021008]
- Zhejiang University City College [J-15021]
Using multiple features of the image, the recognition rate of the fruit images can be improved. But with the increase of the number of the fruits, the recognition complex also increased. For reducing the complex degree of the algorithms and meeting demand of lots of fruits recognition, a method based on compressed sensing was proposed. Sixty-three fruit images were investigated, and eight texture feature values and seven shape feature values were extracted to construct the training eigenmatrix. Based on compressed sensing, the sparse coefficient vector which was the sparse representation of the sample eigenvector on the training eigenmatrix can be obtained, so the test sample was classified by analyzing the coefficient vector. Using the different fruits upon a blank background in this paper, the recognition rates of this method are 83%. The experimental results showed that the recognition method based on compressed sensing could effectively recognize the different class fruits.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据